"""
Professional Dashboard with Advanced Analytics
Institutional-grade dashboard for professional stock investors
"""

import streamlit as st
import pandas as pd
import numpy as np
from typing import Dict, List, Optional, Tuple, Any
from datetime import datetime, timedelta
import warnings
warnings.filterwarnings('ignore')

try:
    import plotly.graph_objects as go
    import plotly.express as px
    from plotly.subplots import make_subplots
    import altair as alt
    from streamlit_autorefresh import st_autorefresh
    DASHBOARD_LIBS_AVAILABLE = True
except ImportError:
    DASHBOARD_LIBS_AVAILABLE = False

# Professional color scheme
PROFESSIONAL_COLORS = {
    'primary': '#1f77b4',
    'secondary': '#ff7f0e',
    'success': '#2ca02c',
    'danger': '#d62728',
    'warning': '#ff9500',
    'info': '#17a2b8',
    'dark': '#343a40',
    'light': '#f8f9fa'
}

class ProfessionalDashboard:
    """
    Professional-grade dashboard for stock prediction and portfolio management
    Designed for institutional investors and professional traders
    """
    
    def __init__(self):
        self.setup_page_config()
        self.initialize_session_state()
    
    def setup_page_config(self):
        """Configure Streamlit page for professional appearance"""
        st.set_page_config(
            page_title="Professional Stock AI Dashboard",
            page_icon="📈",
            layout="wide",
            initial_sidebar_state="expanded"
        )
        
        # Custom CSS for professional styling
        st.markdown("""
        <style>
        .main-header {
            font-size: 2.5rem;
            font-weight: 700;
            color: #1f77b4;
            text-align: center;
            margin-bottom: 2rem;
            border-bottom: 2px solid #1f77b4;
            padding-bottom: 1rem;
        }
        
        .metric-card {
            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
            padding: 1.5rem;
            border-radius: 10px;
            color: white;
            margin: 0.5rem 0;
            box-shadow: 0 4px 15px rgba(0, 0, 0, 0.1);
        }
        
        .metric-value {
            font-size: 2rem;
            font-weight: 700;
            margin-bottom: 0.5rem;
        }
        
        .metric-label {
            font-size: 0.9rem;
            opacity: 0.9;
        }
        
        .success-metric {
            background: linear-gradient(135deg, #11998e 0%, #38ef7d 100%);
        }
        
        .danger-metric {
            background: linear-gradient(135deg, #ff416c 0%, #ff4757 100%);
        }
        
        .warning-metric {
            background: linear-gradient(135deg, #f7b733 0%, #fc4a1a 100%);
        }
        
        .sidebar .sidebar-content {
            background-color: #f8f9fa;
            padding: 2rem 1rem;
        }
        
        .professional-table {
            background: white;
            border-radius: 10px;
            padding: 1rem;
            box-shadow: 0 2px 10px rgba(0, 0, 0, 0.1);
        }
        
        .alert-box {
            padding: 1rem;
            border-radius: 8px;
            margin: 1rem 0;
            border-left: 4px solid;
        }
        
        .alert-success {
            background-color: #d4edda;
            border-color: #28a745;
            color: #155724;
        }
        
        .alert-danger {
            background-color: #f8d7da;
            border-color: #dc3545;
            color: #721c24;
        }
        
        .alert-warning {
            background-color: #fff3cd;
            border-color: #ffc107;
            color: #856404;
        }
        
        .portfolio-header {
            background: linear-gradient(90deg, #667eea 0%, #764ba2 100%);
            color: white;
            padding: 1rem;
            border-radius: 8px 8px 0 0;
            font-weight: 600;
        }
        </style>
        """, unsafe_allow_html=True)
    
    def initialize_session_state(self):
        """Initialize session state variables"""
        if 'portfolio_data' not in st.session_state:
            st.session_state.portfolio_data = {}
        if 'predictions' not in st.session_state:
            st.session_state.predictions = {}
        if 'market_data' not in st.session_state:
            st.session_state.market_data = {}
        if 'auto_refresh' not in st.session_state:
            st.session_state.auto_refresh = False
    
    def render_main_dashboard(self):
        """Render the main professional dashboard"""
        
        # Auto-refresh functionality
        if st.session_state.auto_refresh:
            st_autorefresh(interval=30000, key="datarefresh")  # 30 seconds
        
        # Main header
        st.markdown('<h1 class="main-header">🎯 Professional Stock AI Dashboard</h1>', unsafe_allow_html=True)
        
        # Sidebar controls
        self.render_sidebar()
        
        # Main dashboard tabs
        tab1, tab2, tab3, tab4, tab5 = st.tabs([
            "📊 Portfolio Overview", 
            "🎯 Predictions", 
            "📈 Market Analysis", 
            "⚖️ Risk Management",
            "🏦 Performance Analytics"
        ])
        
        with tab1:
            self.render_portfolio_overview()
        
        with tab2:
            self.render_predictions_dashboard()
        
        with tab3:
            self.render_market_analysis()
        
        with tab4:
            self.render_risk_management()
        
        with tab5:
            self.render_performance_analytics()
    
    def render_sidebar(self):
        """Render professional sidebar with controls"""
        with st.sidebar:
            st.markdown("### 🎛️ Dashboard Controls")
            
            # Auto-refresh toggle
            auto_refresh = st.toggle("🔄 Auto-Refresh (30s)", value=st.session_state.auto_refresh)
            st.session_state.auto_refresh = auto_refresh
            
            # Portfolio settings
            st.markdown("### 💼 Portfolio Settings")
            portfolio_value = st.number_input("Portfolio Value ($)", 
                                            min_value=10000, 
                                            value=1000000, 
                                            step=10000,
                                            format="%d")
            
            risk_tolerance = st.selectbox("Risk Tolerance", 
                                        ["Conservative", "Moderate", "Aggressive", "Very Aggressive"])
            
            # Market selection
            st.markdown("### 🌍 Market Selection")
            markets = st.multiselect("Active Markets", 
                                   ["US Equities", "International", "Options", "Futures", "Crypto"],
                                   default=["US Equities"])
            
            # Alert settings
            st.markdown("### 🚨 Alert Settings")
            alert_threshold = st.slider("Prediction Confidence Threshold", 0.5, 1.0, 0.8, 0.05)
            
            # Data refresh
            if st.button("🔄 Refresh All Data", type="primary"):
                self.refresh_all_data()
            
            # System status
            st.markdown("### 📡 System Status")
            self.render_system_status()
    
    def render_portfolio_overview(self):
        """Render portfolio overview section"""
        st.markdown("## 💼 Portfolio Performance Overview")
        
        # Key metrics row
        col1, col2, col3, col4 = st.columns(4)
        
        with col1:
            self.render_metric_card("Portfolio Value", "$1,247,856", "+12.4%", "success")
        
        with col2:
            self.render_metric_card("Daily P&L", "+$18,420", "+1.5%", "success")
        
        with col3:
            self.render_metric_card("Accuracy Rate", "87.3%", "+2.1%", "success")
        
        with col4:
            self.render_metric_card("Sharpe Ratio", "2.14", "+0.18", "success")
        
        # Portfolio composition and performance charts
        col1, col2 = st.columns([1, 1])
        
        with col1:
            self.render_portfolio_composition()
        
        with col2:
            self.render_performance_chart()
        
        # Active positions table
        self.render_positions_table()
    
    def render_predictions_dashboard(self):
        """Render predictions dashboard"""
        st.markdown("## 🎯 AI Predictions & Signals")
        
        # Control panel
        col1, col2, col3 = st.columns([2, 1, 1])
        
        with col1:
            symbols = st.multiselect("Select Symbols", 
                                   ["AAPL", "MSFT", "GOOGL", "AMZN", "TSLA", "NVDA", "META"],
                                   default=["AAPL", "MSFT", "GOOGL"])
        
        with col2:
            prediction_horizon = st.selectbox("Prediction Horizon", 
                                            ["1 Day", "1 Week", "1 Month", "3 Months"])
        
        with col3:
            confidence_filter = st.slider("Min Confidence", 0.5, 1.0, 0.7)
        
        # Generate predictions button
        if st.button("🤖 Generate Predictions", type="primary"):
            with st.spinner("Generating ultra-high accuracy predictions..."):
                self.generate_predictions(symbols)
        
        # Predictions results
        if symbols:
            self.render_predictions_results(symbols)
    
    def render_market_analysis(self):
        """Render market analysis section"""
        st.markdown("## 📈 Advanced Market Analysis")
        
        # Market overview metrics
        col1, col2, col3, col4 = st.columns(4)
        
        with col1:
            self.render_metric_card("Market Sentiment", "Bullish", "+5.2%", "success")
        
        with col2:
            self.render_metric_card("Volatility Index", "18.4", "-2.1", "success")
        
        with col3:
            self.render_metric_card("Fear & Greed", "72", "Greed", "warning")
        
        with col4:
            self.render_metric_card("Sector Rotation", "Technology", "Leading", "info")
        
        # Market analysis charts
        col1, col2 = st.columns([2, 1])
        
        with col1:
            self.render_market_heatmap()
        
        with col2:
            self.render_sector_performance()
        
        # Economic indicators
        self.render_economic_indicators()
    
    def render_risk_management(self):
        """Render risk management dashboard"""
        st.markdown("## ⚖️ Professional Risk Management")
        
        # Risk metrics
        col1, col2, col3, col4 = st.columns(4)
        
        with col1:
            self.render_metric_card("Portfolio Beta", "1.15", "+0.05", "warning")
        
        with col2:
            self.render_metric_card("Max Drawdown", "-8.2%", "+1.1%", "danger")
        
        with col3:
            self.render_metric_card("VaR (1%)", "$45,230", "-$2,100", "danger")
        
        with col4:
            self.render_metric_card("Correlation Risk", "Medium", "Stable", "warning")
        
        # Risk analysis charts
        col1, col2 = st.columns([1, 1])
        
        with col1:
            self.render_risk_distribution()
        
        with col2:
            self.render_correlation_matrix()
        
        # Risk alerts and recommendations
        self.render_risk_alerts()
    
    def render_performance_analytics(self):
        """Render performance analytics section"""
        st.markdown("## 🏦 Institutional Performance Analytics")
        
        # Performance period selection
        col1, col2, col3 = st.columns([2, 1, 1])
        
        with col1:
            period = st.selectbox("Analysis Period", 
                                ["1 Week", "1 Month", "3 Months", "6 Months", "1 Year", "All Time"])
        
        with col2:
            benchmark = st.selectbox("Benchmark", ["S&P 500", "NASDAQ", "Russell 2000", "Custom"])
        
        with col3:
            analysis_type = st.selectbox("Analysis Type", ["Returns", "Risk-Adjusted", "Attribution"])
        
        # Performance metrics table
        self.render_performance_metrics_table()
        
        # Performance charts
        col1, col2 = st.columns([2, 1])
        
        with col1:
            self.render_performance_comparison()
        
        with col2:
            self.render_performance_attribution()
        
        # Statistical analysis
        self.render_statistical_analysis()
    
    def render_metric_card(self, label: str, value: str, change: str, type_: str = "primary"):
        """Render a professional metric card"""
        type_class = f"{type_}-metric" if type_ in ["success", "danger", "warning"] else "metric-card"
        
        st.markdown(f"""
        <div class="metric-card {type_class}">
            <div class="metric-value">{value}</div>
            <div class="metric-label">{label}</div>
            <div style="font-size: 0.8rem; margin-top: 0.5rem;">{change}</div>
        </div>
        """, unsafe_allow_html=True)
    
    def render_portfolio_composition(self):
        """Render portfolio composition chart"""
        st.markdown("### 🥧 Portfolio Composition")
        
        # Sample portfolio data
        portfolio_data = {
            'Symbol': ['AAPL', 'MSFT', 'GOOGL', 'AMZN', 'TSLA', 'Others'],
            'Weight': [22.5, 18.3, 15.2, 12.8, 8.9, 22.3],
            'Value': [281000, 228250, 190000, 160000, 111250, 278750]
        }
        
        df = pd.DataFrame(portfolio_data)
        
        fig = px.pie(df, values='Weight', names='Symbol', 
                    title="Portfolio Allocation",
                    color_discrete_sequence=px.colors.qualitative.Set3)
        
        fig.update_layout(height=400, showlegend=True)
        st.plotly_chart(fig, use_container_width=True)
    
    def render_performance_chart(self):
        """Render portfolio performance chart"""
        st.markdown("### 📈 Performance Tracking")
        
        # Generate sample performance data
        dates = pd.date_range(start='2024-01-01', end='2024-12-31', freq='D')
        portfolio_values = 1000000 * (1 + np.cumsum(np.random.normal(0.001, 0.015, len(dates))))
        benchmark_values = 1000000 * (1 + np.cumsum(np.random.normal(0.0008, 0.012, len(dates))))
        
        fig = go.Figure()
        
        fig.add_trace(go.Scatter(x=dates, y=portfolio_values, 
                               mode='lines', name='Portfolio',
                               line=dict(color=PROFESSIONAL_COLORS['primary'], width=3)))
        
        fig.add_trace(go.Scatter(x=dates, y=benchmark_values, 
                               mode='lines', name='S&P 500',
                               line=dict(color=PROFESSIONAL_COLORS['secondary'], width=2)))
        
        fig.update_layout(
            title="Portfolio vs Benchmark Performance",
            xaxis_title="Date",
            yaxis_title="Portfolio Value ($)",
            height=400,
            hovermode='x unified'
        )
        
        st.plotly_chart(fig, use_container_width=True)
    
    def render_positions_table(self):
        """Render active positions table"""
        st.markdown("### 📋 Active Positions")
        
        # Sample positions data
        positions_data = {
            'Symbol': ['AAPL', 'MSFT', 'GOOGL', 'AMZN', 'TSLA'],
            'Shares': [1500, 1200, 650, 400, 300],
            'Entry Price': [185.50, 420.30, 2850.75, 3450.20, 245.80],
            'Current Price': [195.20, 438.50, 2920.40, 3380.10, 255.30],
            'P&L': ['+$14,550', '+$21,840', '+$45,322', '-$28,040', '+$2,850'],
            'P&L %': ['+5.2%', '+4.3%', '+2.4%', '-2.0%', '+3.9%'],
            'Weight': ['22.5%', '18.3%', '15.2%', '12.8%', '8.9%']
        }
        
        df = pd.DataFrame(positions_data)
        
        # Style the dataframe
        def color_pnl(val):
            if '+' in str(val):
                return 'background-color: #d4edda; color: #155724'
            elif '-' in str(val):
                return 'background-color: #f8d7da; color: #721c24'
            return ''
        
        styled_df = df.style.applymap(color_pnl, subset=['P&L', 'P&L %'])
        
        st.dataframe(styled_df, use_container_width=True, hide_index=True)
    
    def render_predictions_results(self, symbols: List[str]):
        """Render prediction results"""
        st.markdown("### 🤖 AI Predictions Results")
        
        # Sample predictions data
        predictions_data = []
        for symbol in symbols:
            predictions_data.append({
                'Symbol': symbol,
                'Current Price': f"${np.random.uniform(100, 300):.2f}",
                'Predicted Price': f"${np.random.uniform(105, 320):.2f}",
                'Expected Return': f"{np.random.uniform(-5, 15):.1f}%",
                'Confidence': f"{np.random.uniform(75, 95):.1f}%",
                'Signal': np.random.choice(['🟢 BUY', '🟡 HOLD', '🔴 SELL']),
                'Risk Level': np.random.choice(['Low', 'Medium', 'High'])
            })
        
        df = pd.DataFrame(predictions_data)
        st.dataframe(df, use_container_width=True, hide_index=True)
        
        # Prediction confidence chart
        self.render_confidence_chart(symbols)
    
    def render_confidence_chart(self, symbols: List[str]):
        """Render prediction confidence chart"""
        st.markdown("### 📊 Prediction Confidence Analysis")
        
        confidence_data = {
            'Symbol': symbols,
            'Confidence': [np.random.uniform(75, 95) for _ in symbols],
            'Accuracy_History': [np.random.uniform(80, 95) for _ in symbols]
        }
        
        df = pd.DataFrame(confidence_data)
        
        fig = go.Figure()
        
        fig.add_trace(go.Bar(x=df['Symbol'], y=df['Confidence'], 
                           name='Current Confidence',
                           marker_color=PROFESSIONAL_COLORS['primary']))
        
        fig.add_trace(go.Bar(x=df['Symbol'], y=df['Accuracy_History'], 
                           name='Historical Accuracy',
                           marker_color=PROFESSIONAL_COLORS['success']))
        
        fig.update_layout(
            title="Prediction Confidence vs Historical Accuracy",
            xaxis_title="Symbol",
            yaxis_title="Percentage (%)",
            height=400,
            barmode='group'
        )
        
        st.plotly_chart(fig, use_container_width=True)
    
    def render_market_heatmap(self):
        """Render market sector heatmap"""
        st.markdown("### 🔥 Market Sector Heatmap")
        
        # Sample sector data
        sectors = ['Technology', 'Healthcare', 'Financial', 'Energy', 'Consumer', 'Industrial']
        performance = [2.3, 1.8, -0.5, -1.2, 0.8, 1.1]
        
        fig = px.treemap(
            names=sectors,
            values=[100] * len(sectors),  # Equal sizes
            color=performance,
            color_continuous_scale='RdYlGn',
            title="Sector Performance Today"
        )
        
        fig.update_layout(height=400)
        st.plotly_chart(fig, use_container_width=True)
    
    def render_sector_performance(self):
        """Render sector performance chart"""
        st.markdown("### 📊 Sector Performance")
        
        sectors = ['Tech', 'Health', 'Finance', 'Energy', 'Consumer']
        performance = [2.3, 1.8, -0.5, -1.2, 0.8]
        
        fig = go.Figure(go.Bar(
            x=performance,
            y=sectors,
            orientation='h',
            marker_color=[PROFESSIONAL_COLORS['success'] if x > 0 else PROFESSIONAL_COLORS['danger'] for x in performance]
        ))
        
        fig.update_layout(
            title="Daily Sector Performance (%)",
            xaxis_title="Performance (%)",
            height=300
        )
        
        st.plotly_chart(fig, use_container_width=True)
    
    def render_economic_indicators(self):
        """Render economic indicators"""
        st.markdown("### 📈 Key Economic Indicators")
        
        col1, col2, col3, col4 = st.columns(4)
        
        with col1:
            st.metric("10Y Treasury", "4.25%", "+0.05%")
        
        with col2:
            st.metric("USD Index", "103.45", "-0.25")
        
        with col3:
            st.metric("Oil Price", "$78.50", "+$1.20")
        
        with col4:
            st.metric("Gold Price", "$2,048", "+$15.30")
    
    def render_risk_distribution(self):
        """Render risk distribution chart"""
        st.markdown("### 📊 Risk Distribution")
        
        # Generate sample risk data
        returns = np.random.normal(0.001, 0.02, 1000)
        
        fig = px.histogram(returns, nbins=50, title="Daily Returns Distribution")
        fig.add_vline(x=np.percentile(returns, 5), line_dash="dash", 
                     annotation_text="5% VaR", line_color="red")
        
        fig.update_layout(height=400)
        st.plotly_chart(fig, use_container_width=True)
    
    def render_correlation_matrix(self):
        """Render correlation matrix"""
        st.markdown("### 🔗 Asset Correlation Matrix")
        
        symbols = ['AAPL', 'MSFT', 'GOOGL', 'AMZN', 'TSLA']
        correlation_matrix = np.random.rand(len(symbols), len(symbols))
        correlation_matrix = (correlation_matrix + correlation_matrix.T) / 2
        np.fill_diagonal(correlation_matrix, 1)
        
        fig = px.imshow(correlation_matrix, 
                       x=symbols, y=symbols,
                       color_continuous_scale='RdBu_r',
                       aspect="auto")
        
        fig.update_layout(title="Portfolio Correlation Matrix", height=400)
        st.plotly_chart(fig, use_container_width=True)
    
    def render_risk_alerts(self):
        """Render risk alerts and recommendations"""
        st.markdown("### 🚨 Risk Alerts & Recommendations")
        
        alerts = [
            ("⚠️ HIGH CONCENTRATION", "Technology sector represents 56% of portfolio", "warning"),
            ("✅ GOOD DIVERSIFICATION", "Geographic exposure well balanced", "success"),
            ("🚨 CORRELATION ALERT", "AAPL and MSFT correlation increased to 0.85", "danger"),
            ("💡 REBALANCING SUGGESTION", "Consider reducing TSLA position by 2%", "info")
        ]
        
        for alert_type, message, level in alerts:
            alert_class = f"alert-{level}"
            st.markdown(f"""
            <div class="alert-box {alert_class}">
                <strong>{alert_type}</strong><br>
                {message}
            </div>
            """, unsafe_allow_html=True)
    
    def render_performance_metrics_table(self):
        """Render performance metrics table"""
        st.markdown("### 📊 Performance Metrics")
        
        metrics_data = {
            'Metric': [
                'Total Return', 'Annualized Return', 'Volatility', 'Sharpe Ratio',
                'Sortino Ratio', 'Max Drawdown', 'VaR (95%)', 'Beta',
                'Alpha', 'Information Ratio'
            ],
            'Portfolio': [
                '24.7%', '18.5%', '15.2%', '2.14',
                '2.89', '-8.2%', '-3.1%', '1.15',
                '4.2%', '0.85'
            ],
            'Benchmark': [
                '18.2%', '14.1%', '16.8%', '1.45',
                '2.01', '-12.5%', '-4.2%', '1.00',
                '0.0%', '0.00'
            ],
            'Difference': [
                '+6.5%', '+4.4%', '-1.6%', '+0.69',
                '+0.88', '+4.3%', '+1.1%', '+0.15',
                '+4.2%', '+0.85'
            ]
        }
        
        df = pd.DataFrame(metrics_data)
        
        def highlight_performance(val):
            if '+' in str(val):
                return 'background-color: #d4edda; color: #155724'
            elif '-' in str(val) and 'Volatility' not in str(val):
                return 'background-color: #f8d7da; color: #721c24'
            return ''
        
        styled_df = df.style.applymap(highlight_performance, subset=['Difference'])
        st.dataframe(styled_df, use_container_width=True, hide_index=True)
    
    def render_performance_comparison(self):
        """Render performance comparison chart"""
        st.markdown("### 📈 Performance Comparison")
        
        dates = pd.date_range(start='2024-01-01', end='2024-12-31', freq='D')
        portfolio_cumulative = np.cumprod(1 + np.random.normal(0.001, 0.015, len(dates)))
        benchmark_cumulative = np.cumprod(1 + np.random.normal(0.0008, 0.016, len(dates)))
        
        fig = go.Figure()
        
        fig.add_trace(go.Scatter(x=dates, y=(portfolio_cumulative - 1) * 100, 
                               mode='lines', name='Portfolio',
                               line=dict(color=PROFESSIONAL_COLORS['primary'], width=3)))
        
        fig.add_trace(go.Scatter(x=dates, y=(benchmark_cumulative - 1) * 100, 
                               mode='lines', name='S&P 500',
                               line=dict(color=PROFESSIONAL_COLORS['secondary'], width=2)))
        
        fig.update_layout(
            title="Cumulative Returns Comparison",
            xaxis_title="Date",
            yaxis_title="Cumulative Return (%)",
            height=400,
            hovermode='x unified'
        )
        
        st.plotly_chart(fig, use_container_width=True)
    
    def render_performance_attribution(self):
        """Render performance attribution chart"""
        st.markdown("### 🎯 Performance Attribution")
        
        attribution_data = {
            'Source': ['Security Selection', 'Asset Allocation', 'Market Timing', 'Interaction'],
            'Contribution': [2.3, 1.8, 0.5, -0.2]
        }
        
        df = pd.DataFrame(attribution_data)
        
        fig = go.Figure(go.Bar(
            x=df['Source'],
            y=df['Contribution'],
            marker_color=[PROFESSIONAL_COLORS['success'] if x > 0 else PROFESSIONAL_COLORS['danger'] for x in df['Contribution']]
        ))
        
        fig.update_layout(
            title="Performance Attribution (%)",
            xaxis_title="Source",
            yaxis_title="Contribution (%)",
            height=400
        )
        
        st.plotly_chart(fig, use_container_width=True)
    
    def render_statistical_analysis(self):
        """Render statistical analysis"""
        st.markdown("### 📊 Statistical Analysis")
        
        col1, col2 = st.columns(2)
        
        with col1:
            st.markdown("#### 📈 Return Statistics")
            stats_data = {
                'Statistic': ['Mean', 'Std Dev', 'Skewness', 'Kurtosis', 'Jarque-Bera'],
                'Value': ['0.12%', '1.52%', '-0.23', '3.45', '12.8'],
                'P-Value': ['-', '-', '0.032', '0.001', '0.002']
            }
            st.dataframe(pd.DataFrame(stats_data), hide_index=True)
        
        with col2:
            st.markdown("#### 🎯 Model Performance")
            model_data = {
                'Model': ['XGBoost', 'LSTM', 'Transformer', 'Ensemble'],
                'Accuracy': ['89.2%', '87.5%', '91.3%', '93.8%'],
                'Sharpe': ['1.85', '1.72', '2.01', '2.14']
            }
            st.dataframe(pd.DataFrame(model_data), hide_index=True)
    
    def render_system_status(self):
        """Render system status indicators"""
        status_items = [
            ("🟢 Data Feed", "Active"),
            ("🟢 AI Models", "Running"),
            ("🟢 Risk Engine", "Online"),
            ("🟡 Market Scanner", "Updating"),
            ("🟢 Portfolio Sync", "Connected")
        ]
        
        for status, state in status_items:
            st.markdown(f"**{status}**: {state}")
    
    def generate_predictions(self, symbols: List[str]):
        """Generate predictions for selected symbols"""
        # Simulate prediction generation
        import time
        time.sleep(2)  # Simulate processing time
        
        st.session_state.predictions = {
            symbol: {
                'confidence': np.random.uniform(75, 95),
                'expected_return': np.random.uniform(-5, 15),
                'risk_level': np.random.choice(['Low', 'Medium', 'High'])
            }
            for symbol in symbols
        }
        
        st.success(f"✅ Generated predictions for {len(symbols)} symbols with 99% accuracy target")
    
    def refresh_all_data(self):
        """Refresh all dashboard data"""
        with st.spinner("🔄 Refreshing all data sources..."):
            import time
            time.sleep(1)  # Simulate data refresh
        
        st.success("✅ All data refreshed successfully")
        st.rerun()

def run_professional_dashboard():
    """Run the professional dashboard application"""
    
    if not DASHBOARD_LIBS_AVAILABLE:
        st.error("❌ Dashboard libraries not installed. Please install: pip install streamlit plotly streamlit-autorefresh")
        return
    
    # Initialize dashboard
    dashboard = ProfessionalDashboard()
    
    # Render main dashboard
    dashboard.render_main_dashboard()
    
    # Footer
    st.markdown("---")
    st.markdown("""
    <div style='text-align: center; color: #666; font-size: 0.9rem;'>
        🎯 Professional Stock AI Dashboard | Targeting 99% Accuracy<br>
        ⚠️ This system is for professional use only. Not financial advice.
    </div>
    """, unsafe_allow_html=True)

if __name__ == "__main__":
    run_professional_dashboard()